Groupwise Structural Parcellation of the Cortex: A Sound Approach Based on Logistic Models

International audienceCurrent theories hold that brain function is highly related with long-range physical connections through axonal bundles, namely extrinsic connectivity. However, obtaining a groupwise cortical parcella-tion based on extrinsic connectivity remains challenging. Current par-cellation methods are computationally expensive; need tuning of several parameters or rely on ad-hoc constraints. Furthermore, none of these methods present a model for the cortical extrinsic connectivity. To tackle these problems, we propose a parsimonious model for the extrinsic con-nectivity and an efficient parcellation technique based on clustering of tractograms. Our technique allows the creation of single subject and groupwise parcellations of the whole cortex. The parcellations obtained with our technique are in agreement with anatomical and functional par-cellations in the literature. In particular, the motor and sensory cortex are subdivided in agreement with the human homunculus of Penfield. We illustrate this by comparing our resulting parcels with an anatomical atlas and the motor strip mapping included in the Human Connectome Project data

Automated Morphometric Characterization of the Cerebral Cortex for the Developing and Ageing Brain

Morphometric characterisation of the cerebral cortex can provide information about patterns of brain development and ageing and may be relevant for diagnosis and estimation of the progression of diseases such as Alzheimer's, Huntington's, and schizophrenia. Therefore, understanding and describing the differences between populations in terms of structural volume, shape and thickness is of critical importance. Methodologically, due to data quality, presence of noise, PV effects, limited resolution and pathological variability, the automated, robust and time-consistent estimation of morphometric features is still an unsolved problem. This thesis focuses on the development of tools for robust cross-sectional and longitudinal morphometric characterisation of the human cerebral cortex. It describes techniques for tissue segmentation, structural and morphometric characterisation, cross-sectional and longitudinally cortical thickness estimation from serial MRI images in both adults and neonates. Two new probabilistic brain tissue segmentation techniques are introduced in order to accurately and robustly segment the brain of elderly and neonatal subjects, even in the presence of marked pathology. Two other algorithms based on the concept of multi-atlas segmentation propagation and fusion are also introduced in order to parcelate the brain into its multiple composing structures with the highest possible segmentation accuracy. Finally, we explore the use of the Khalimsky cubic complex framework for the extraction of topologically correct thickness measurements from probabilistic segmentations without explicit parametrisation of the edge. A longitudinal extension of this method is also proposed. The work presented in this thesis has been extensively validated on elderly and neonatal data from several scanners, sequences and protocols. The proposed algorithms have also been successfully applied to breast and heart MRI, neck and colon CT and also to small animal imaging. All the algorithms presented in this thesis are available as part of the open-source package NiftySeg

Probabilistic prediction of Alzheimer’s disease from multimodal image data with Gaussian processes

Alzheimer’s disease, the most common form of dementia, is an extremely serious health problem, and one that will become even more so in the coming decades as the global population ages. This has led to a massive effort to develop both new treatments for the condition and new methods of diagnosis; in fact the two are intimately linked as future treatments will depend on earlier diagnosis, which in turn requires the development of biomarkers that can be used to identify and track the disease. This is made possible by studies such as the Alzheimer’s disease neuroimaging initiative which provides previously unimaginable quantities of imaging and other data freely to researchers. It is the task of early diagnosis that this thesis focuses on. We do so by borrowing modern machine learning techniques, and applying them to image data. In particular, we use Gaussian processes (GPs), a previously neglected tool, and show they can be used in place of the more widely used support vector machine (SVM). As combinations of complementary biomarkers have been shown to be more useful than the biomarkers are individually, we go on to show GPs can also be applied to integrate different types of image and non-image data, and thanks to their properties this improves results further than it does with SVMs. In the final two chapters, we also look at different ways to formulate both the prediction of conversion to Alzheimer’s disease as a machine learning problem and the way image data can be used to generate features for input as a machine learning algorithm. Both of these show how unconventional approaches may improve results. The result is an advance in the state-of-the-art for a very clinically important problem, which may prove useful in practice and show a direction of future research to further increase the usefulness of such method

Simultaneous PET/MRI for Connectivity Mapping: Quantitative Methods in Clinical Setting

In recent years, the study of brain connectivity has received growing interest from neuroscience field, from a point of view both of analysis of pathological condition and of a healthy brain. Hybrid PET/MRI scanners are promising tools to study this complex phenomenon. This thesis presents a general framework for the acquisition and analysis of simultaneous multi-modal PET/MRI imaging data to study brain connectivity in a clinical setting. Several aspects are faced ranging from the planning of an acquisition protocol consistent with clinical constraint to the off-line PET image reconstruction, from the selection and implementation of methods for quantifying the acquired data to the development of methodologies to combine the complementary information obtained with the two modalities. The developed analysis framework was applied to two different studies, a first conducted on patients affected by Parkinson’s Disease and dementia, and a second one on high grade gliomas, as proof of concept evaluation that the pipeline can be extended in clinical settings

Building functional neuromarkers from resting state fMRI to describe physiopathological traits

2016 - 2017The overarching goal of this work has been that of devising novel methods for building functional neuromarkers from resting-state fMRI data to describe healthy and pathological human behaviour. Observing spontaneous uctuations of the BOLD signal, resting-state fMRI allows to have an insight into the functional organisation of the brain and to detect functional networks that are consistent across subjects. Studying how patterns of functional connectivity vary both in healthy subjects and in subjects a ected by a neurodegenerative disease is a way to shed light on the physiological and pathological mechanisms governing our nervous system. The rst part of this thesis is devoted to the description of fully data-driven feature extraction techniques based on clustering aimed at supporting the diagnosis of neurodegenerative diseases (e.g., amyotrophic lateral sclerosis and Parkinson's disease). The high-dimensional nature of resting state fMRI data implies the need of suitable feature selection techniques. Traditional univariate techniques are fast and straightforward to interpret, but are unable to unveil relationships among multiple features. For this reason, this work presents a methodology based on consensus clustering, a particular approach to the clustering problem that consists in combining di erent partitions of the same data set to produce more stable solutions. One of the objectives of fMRI data analysis is to determine regions that show an abnormal activity with respect to a healthy brain and this is often attained with comparative statistical models applied to single voxels or brain parcels within one or several functional networks. Here, stochastic rank aggregation is applied to identify brain regions that exhibit a coherent behaviour in groups of subjects a ected by the same disorder. The proposed methodology was validated on real data and the results are consistent with previous literature, thus indicating that this approach might be suitable to support early diagnosis of neurodegenerative diseases... [edited by Author]XXX cicl